How Do Adaptive Learning Expectations Rationalize Stronger Monetary Policy Response in Brazil?
IMF Working Papers, January 27, 2023
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Bibliographic details
- Authors: Allan Dizioli, Hou Wang
- Published: January 27, 2023
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9798400229954.001
Research question and model
- Estimates a standard Dynamic Stochastic General Equilibrium (DSGE) model that includes a wage and price Phillip's curves with different expectation formation processes for Brazil and the USA.
- Compares two expectation formation processes:
- Rational expectation process.
- Adaptive learning model (limited rationality).
Key empirical findings
- The adaptive learning model does a better job in fitting the data in both Brazil and the USA.
- Expectations are more backward-looking and started to drift away sooner in 2021 in Brazil than in the USA.
- The separate inclusion of a labor market in the model helps to anchor inflation even when:
- Expectations are adaptive,
- There is a positive output gap,
- Inflation is above target.
Optimal policy exercises and policy implications
- Optimal policy exercises prescribe early monetary policy tightening in the context of:
- Positive output gaps,
- Inflation far above the central bank target.
- Implication: Stronger and earlier monetary policy responses are rationalized when expectations follow adaptive learning and labor-market dynamics are accounted for.
Subjects and keywords
- Subjects: Central bank policy rate, Economic sectors, Financial crises, Financial services, Inflation, Labor, Output gap, Prices, Production, Real wages, Wage gap.
- Keywords: Bayesian estimation., Central bank policy rate, DSGE, Forecasting and Simulation, Global, Inflation, Inflation dynamics, inflation expectation, learning expectation, optimal monetary policy, Output gap, Real wages, Wage gap, wages expectation.
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- Working Paper